Nested Cross-Validation for scikit-learn using MPI

This package provides nested cross-validation similar to scikit-learn's GridSearchCV but uses the Message Passing Interface (MPI) for parallel computing.

Requirements

scikit-learn 0.16.0 or later
mpi4py
pandas

Example

from mpi4py import MPI
import numpy
from sklearn.datasets import load_boston
from sklearn.svm import SVR
from grid_search import NestedGridSearchCV

data = load_boston()
X = data['data']
y = data['target']

estimator = SVR(max_iter=1000, tol=1e-5)

param_grid = {'C': 2. ** numpy.arange(-5, 15, 2),
              'gamma': 2. ** numpy.arange(3, -15, -2),
              'kernel': ['poly', 'rbf']}

nested_cv = NestedGridSearchCV(estimator, param_grid, 'mean_absolute_error',
                               cv=5, inner_cv=3)
nested_cv.fit(X, y)

if MPI.COMM_WORLD.Get_rank() == 0:
    for i, scores in enumerate(nested_cv.grid_scores_):
        scores.to_csv('grid-scores-%d.csv' % (i + 1), index=False)

    print(nested_cv.best_params_)

The result should look like this:

	score (Validation)	C	gamma	kernel	score (Test)
fold
1	-7.252490	0.5	0.000122	rbf	-4.178257
2	-5.662221	128.0	0.000122	rbf	-5.445915
3	-5.582780	32.0	0.000122	rbf	-7.066123
4	-6.306561	0.5	0.000122	rbf	-6.059503
5	-6.174779	128.0	0.000122	rbf	-6.606218

shengwa/scikit-learn-mpi-grid-search

Nested Cross-Validation for scikit-learn using MPI

Requirements

Example